This final report of the "Adding Clinical Data to Statewide Administrative Data Pilot Project" details the processes of recruiting hospitals for the project, extracting laboratory data and normalizing the laboratory terminology into the Logical Observation Identifiers Names and Codes (LOINC) standard, submitting the data, linking the clinical and administrative datasets and assessing the added value of using clinical data to better predict complications leading to mortality in the hospitals.
The Agency for Healthcare Administration (AHCA), Florida Center for Health Information and Policy Analysis, was awarded a contract from the Agency for Healthcare Research and Quality (AHRQ) that ran from October 2007 through December 2009 for a pilot project to assess the resources required to standardize laboratory data and to study how to use the clinical laboratory data to better predict complications leading to mortality. The AHRQ pilot project’s goals were to demonstrate and evaluate the process required to
The Agency recruited a total of twenty two hospitals and developed a Data Sharing Agreement with the participating hospitals to support acquisition of clinical data for linkage to existing Agency information.
The Agency worked with 3M Health Information Systems, Inc. and the participating hospitals to map their laboratory values to standardized LOINC terminology and to evaluate the extent to which the 3M HIS risk-adjustment model can be made more accurate with the availability of the clinical data.
The hospitals sent their laboratory data catalogues to 3M Terminology Consulting Services (TCS) to initiate the LOINC mapping. The data collected for the pilot project consisted of specific clinical laboratory data elements and a set of demographic indicators that were used to link the clinical data with the administrative data. 3M TCS worked with each hospital and provided technical assistance to each hospital’s quality and technical staff to standardize its laboratory data terminology and values and to verify accuracy of the final normalized map of laboratory values. 3M TCS validated the correct standardization of laboratory values to LOINC through an iterative process with each hospital.
The hospitals extracted three quarters of laboratory and blood culture data, based on admissions from April 1, 2007 to December 31, 2007 for all patients and for all laboratory tests conducted. They then applied the LOINC mapping to convert their unique laboratory values to LOINC standardized values and terminology. After conducting quality assurance to ensure that the data mapping was correct, they uploaded the standardized laboratory dataset as text files using Tab Separated Values to a secure File Transfer Protocol (FTP) site at AHCA.
The Agency loaded the demographic, blood culture, and clinical lab data received from hospitals and the existing administrative inpatient data onto a secure network server. The clinical and administrative datasets were validated; de-identified and all confidential data fields were deleted from the administrative files. The AHCA project team performed a quality assurance check by matching the records using inpatient ID’s and then with the newly created ID and compared the results of the matches. The Agency uploaded the files to 3M HIS using their secure FTP site.
3M HIS applied data screening criteria to create a linked administrative and clinical laboratory test analysis dataset. Utilizing the Present on Admission indicator for each diagnosis code, 3M HIS assigned both an admission and discharge APR DRG and risk of mortality subclass to each patient. 3M HIS next created test result ranges for each of the laboratory tests that could be evaluated for their ability to improve the APR DRG prediction of mortality. Using research literature and clinical input, 3M HIS identified meaningful results outside normal ranges of laboratory tests and then used statistical tests to identify the subset of clinical laboratory test result specifications that improved the performance of APR DRGs for predicting inpatient mortality. The final step in the analysis was to assess the overall incremental improvement due to the addition of the clinical laboratory test results on the accuracy of the risk adjustment models for predicting inpatient mortality.
The results of the analysis demonstrated that adding selected clinical laboratory data elements to administrative data can improve the accuracy of the risk adjustment models for predicting hospital mortality rates. This preliminary study identified laboratory tests that are relevant for the APR DRG risk of morality prediction, and therefore should constitute the minimum scope of laboratory test results that are included in any mandated collection of selected laboratory test results. The laboratory test results that were found to contribute to increased predictive power were consistent with clinical expectations and constitute a relatively small number of laboratory test results indicative of acute disease. The addition of eleven clinical laboratory test results to the assignment of the admission APR DRG risk of mortality increased the c-statistic and R2 by 0.574 percent and 4.53 percent, respectively. This finding demonstrates that the use of the Present on Admission indicator along with the incorporation of selected clinical data elements such as laboratory test results can lead to better assessments of risk of mortality at admission.
3M HIS developed mortality reports based on the admission APR DRG and the model adjusted with clinical laboratory data specific to each hospital. 3M HIS provided a summary of the project results and the hospital mortality reports to the participating hospitals. 3M HIS also submitted a final report of the findings to AHCA.
The findings of this pilot project demonstrate that clinical data, when combined with the Present on Admission indicator and administrative inpatient data, can be used to improved the risk adjustments models to better predict the risk of patient mortality.
The Agency for Healthcare Administration (AHCA), Florida Center for Health Information and Policy Analysis, was awarded a two year contract from the Agency for Healthcare Research and Quality (AHRQ) for a pilot project to study advanced methods of predicting hospital complications. The project involved standardizing laboratory values using Logical Observation Identifiers Names and Codes (LOINC) terminology joined to Present on Admission indicators and hospital administrative data collected by the Agency to better assess the added value of these combined indicators for analyzing hospital quality measures. The project ran from October 2007 through December 2009.
By adding clinical data to administrative data, the project team expected to fulfill the AHRQ pilot project’s goals to demonstrate and evaluate the processes required to:
Figure 1: Project Diagram
Circular diagram of project stages with project description in center:
Project description: Adding Clinical Data to Statewide Administrative Data
The AHRQ pilot project’s goals are to demonstrate and evaluate the process required to join the clinical laboratory data with the administrative data, to assess the quality of patient care within hospitals and to test the improvement in predicting potential complications by adding the POA indicator and the clinical laboratory data to the administrative data.
Step 1: Contracts and approvals
Step 2: Hospital recruitment
Step 3: LOINC mapping
Step 4: Data transmission
Step 5: Merging data
Step 6: Data Analysis
Finish: Final Report
The process involves ongoing communications throughout the entire cycle of steps.
The Agency is authorized by statute to collect administrative data from every hospital in Florida. The Present on Admission (POA) indicator was added to the administrative dataset in 2007. Consequently, the Agency was ready to undertake the data collection and oversee the research part of the project,
The Agency’s project team came from the Office of Health Information Exchange in the Florida Center for Health Information and Policy Analysis, directed by Christine Nye. The team consisted of a project director, project coordinator, project accountant and a project laboratory subject matter expert. Table 1 lists the names and the responsibilities of the Agency's team involved in this pilot project.
Table 1: Florida Agency's Team Members and their Responsibilities
|AHCA Team Members||Functional Area of Responsibility|
|Christopher Sullivan, Ph.D.||Project Director|
|Bahia Diefenbach, Ph.D.||Project Coordinator|
|Brenda Phinney||Project Accountant|
|Nancy Carvallo||Project Laboratory Subject Matter Expert|
A total of twenty two hospitals took part in the pilot study. These participating hospitals included Broward Health, Memorial Healthcare System, BayCare Health System and two independent pediatric hospitals: Miami Children's Hospital and All Children's Hospital. The main team members of the participating hospital systems and hospitals are listed in Table 2.
Table 2: Participating Hospitals' Team Members and their Responsibilities
|Team Members in Participating Hospitals||Functional Area of Responsibility|
|Lisa K. Rawlins||Director of Quality and Performance Improvement|
|Peter Barnick||Systems Analyst|
|Doris Crain||VP/Chief Information Officer|
|Yvette Herrera||Clinical Systems Integration Manager|
|Tony Ruiz||Director/Project Management Office Information Systems|
|Connie Thornton||Coordinator – Quality and Performance Improvement|
|Memorial Healthcare System|
|Forest Blanton||Chief Information Officer|
|Gary Fuller||Manager, Information Technology|
|Anita Wilson||Director, Clinical Systems, Information Technology|
|Jeffrey Sturman||Administrative Director, Business Systems, Information Technology|
|S. Friedman||Manager, Decision Support|
|BayCare Health System|
|Denise Remus, Ph.D.||Chief Quality Officer|
|Victor Hruszczyk||Vice President of Laboratory Services|
|All Children's Hospital|
|Cal Popovitch||Chief Information Officer|
|Michael Epstein, M.D||Senior Vice President Medical Affairs|
|Mike Isaacs||Lab Systems Analyst|
|Miami Children's Hospital|
|Redmond Burke, M.D||Chief, Division of Cardiovascular Surgery|
|John Madril||Outcomes Research Manager|
|Raul Herrera||Chief Research Officer|
3M Health Information Systems (HIS) was contracted to assist hospitals in the research project, to analyze the joined datasets and to translate the naming convention for their laboratory tests to LOINC. 3M worked with the hospital teams to introduce the LOINC vocabulary standard, to define the extract data necessary, to monitor the data mapping and to resolve problems where necessary. Another major task performed by 3M HIS was to analyze the resulting dataset created by joining the laboratory data and the inpatient administrative data, including POAs, and provide an in-depth analysis on predicting quality indicators in hospitals from the combined laboratory and administrative data.
Table 3: 3M HIS Team Members and their Responsibilities
|3M Team Members||Functional Area of Responsibility|
|Deborah S. Anderson, MBA, PMP||3M Federal Government Program Manager|
|Pam Banning MT(ASCP), PMP||Medical Informatics Terminology Consulting Services - Lab Data Mapping Subject Matter Expert|
|Norbert Goldfield M.D.||Clinical and Economic Research (Analysis)|
|Elizabeth C. McCullough||Senior Research and Development Architect|
The Florida Center for Health Information and Policy Analysis (Florida Center) in the Agency for Health Care Administration (Agency) worked with the State Consumer Health Information and Policy Advisory Council (Council) in all stages of the project. The Council is a stakeholder advisory council for transparency in healthcare information, made up of members who are healthcare professionals committed to Florida’s goal of providing the most accurate and current data for consumer’s use in making healthcare decisions. Table 4 lists the members of the Council.
Table 4: Members of the State Consumer Health Information and Policy Advisory Council
|Thomas W. Arnold||Secretary of the Agency for Healthcare Administration|
|Ana Viamonte Ros, M.D.,M.P.H.||State Surgeon General of the Department of Health|
|Carolyn Timmann||Employee of the Executive Office of the Governor|
|Charles Milsted||Representative of consumers|
|Diane Godfrey||Representative of professional healthcare related association|
|Harry V. Spring||Representative of healthcare purchasers|
|James Bracher, M.B.A.||Representative of Florida Association of Health Plans|
|Susan Douglas||Employee of the Department of Education|
|Karen L. van Caulil, Ph.D.||Representative of local health councils|
|Kim Streit, C.H.E.,M.B.A., M.H.S.||Representative of professional healthcare related association|
|Michael L. Epstein, M.D., Chair||Pediatric Representative of Healthcare Coalition|
|Michael Wasylik, M.D.||Representative of professional healthcare related association|
|Paul Duncan, Ph.D.||Representative of a state university|
|Sally House||Representative of Florida Association of Business/Health Coalitions|
|Mary Beth Senkewicz, J.D.||Employee of the Office of Insurance Regulation|
Aside from the contract with 3M Health Information Systems, the Agency employed no consultants for this project.
Within a month of receiving the AHRQ award, the AHCA project team contacted several hospital systems to encourage the participation of as many hospitals as possible. The team also targeted hospital systems to take advantage of having the LOINC mapping and the extraction of data conducted in one central office. We followed up the first contact with face-to-face meetings in each of the hospitals that agreed to join the project, meeting with the hospital teams and introducing them to the AHRQ pilot project. In the package presented to the hospitals, we included:
The laboratory data elements included in the project were developed in cooperation with 3M Health Information Systems in the original proposal for the project. The project team compared these data elements with those laboratory data elements selected by the other partners in this project, Minnesota and Virginia. We also consulted with Ms. Nancy Carvallo, Project Laboratory Subject Matter Expert at the Agency. In addition, we referred to research in the area of predicting complications using clinical data. One of the research papers we that referred to was a seminal work reported in the Journal of the American Medical Association, Enhancement of Claims Data to Improve Risk Adjustment of Hospital Mortality by Michael Pine, MD, et. al.1
The Agency required approval by the Legislative Budget Committee to amend its budget before the grant funding was initiated and the Agency could receive funds from the Agency for Healthcare Research and Quality (AHRQ) or could enter into contracts with a vendor. The budget amendment establishes spending authority for the agency, and allows setting up of an electronic deposit account for payment of invoices. Budget authority was granted in March 2008, six months after the receipt of the AHRQ contract in September 2007.
Following the budget approval, the AHRQ project team submitted a contract initiation file with the Agency procurement office to begin work on a sole source contract with 3M HIS. However, the project encountered more unexpected delays because the legal office at 3M insisted on changes to the Agency’s contract. The full execution of the 3M HIS contract process took another six months to be completed, in September 2008. Consequent to these budgetary hold-ups, the AHRQ project was initiated with only one year to complete two year’s worth of work.
Another delay occurred during the LOINC mapping phase of the project, when BayCare Health Systems requested a data-sharing agreement to avoid liability from data breaches. This request delayed the collection of lab data from that hospital system and reduced the time available to analyze the data. Additionally, further delays occurred when the firewalls in the hospital security systems refused to allow access to the Agency’s secure FTP to upload datasets. Each one of these delays reduced the time available for the collection and analysis of the data. A timeline of the project is shown in Figure 2.
Figure 2. Timeline of Contracts and Approvals
Timeline of Contracts and Approvals
AHRQ contract awarded: October 2007
AHCA’s approval: October 2007 through March 2008
Hospital Recruitment: October 2007 through April 2008 (22 participating hospitals)
3M HIS contract approval: November 2007 through September 2008
Data sharing agreement: July 2008 through April 2009
AHCA/Hospitals/3M meeting: After September 2008
To identify and recruit hospitals for the pilot project, the Agency's team worked with
The project team was interested in collecting data from large volume hospital systems to maximize the number of records. The team used personal contact to speak with the right person in each hospital, such as the Chief Information Officer or Director of Quality, as an essential step for hospital buy-in. The project team initially contacted the Quality Director of Broward Health System and the Chief Information Officer at Memorial Healthcare System, who welcomed the idea of participating in this pilot project. During our first meeting with the Chief Quality Officer at BayCare Health System, she suggested that instead of collecting data of just the large volume hospitals, we should, collect the data from all the hospitals in their system including the children’s hospital. As a result of that meeting, the project team decided to include several more pediatric hospitals and to conduct a separate analysis for these hospitals, so we sought the participation of two more pediatric hospitals in addition to the three children’s hospitals within the hospital systems.
Following the Legislative Budget Committee’s budget approval in March 2008, the project team contacted the hospitals to set up face to face meetings with each of the hospital systems teams and with the two additional children’s hospitals to introduce them to the program and to explain the requirements of the project (see Appendix 9). It was left to the hospital director’s discretion to invite members of their institution to that meeting. The narrative materials the project team brought with them explained the project, described the laboratory dataset, the LOINC mapping process and provided other details of the project and the responsibilities of the hospitals in working on the project.
The recruitment process varied among the participating hospitals. Some hospitals delayed participation until all higher administration levels approved the project. Other hospitals were on board immediately after the first meeting with them. As a result of our recruitment efforts, we were able to confirm that 22 hospitals would be part of the pilot study, five of which were pediatric hospitals. A map of the hospitals in the project is provided in Figure 3.
Figure 3. Map of Hospital Locations in Florida
Map of Hospital Locations in Florida
Hospital locations are indicated on the map. Hospital names and locations are:
The participants included Broward Health System, Memorial Healthcare System, BayCare Health System and two independent pediatric hospitals: Miami Children’s Hospital and All Children’s Hospital. We were fortunate that the directors or the decision maker at the hospitals and hospital systems were familiar with the issues and the research related to the quality indicators, POA’s, and AHRQ’s efforts and projects in those areas.
During this project, the Florida Center continued to develop relationships with hospital representatives, researchers, clinicians, quality assessment organizations, regional health information organizations, and other key players in the exchange of health information and measurement of healthcare quality. During the two years of the project, we shared the monthly progress report that we submitted to AHRQ with all participating hospitals and the Florida Hospital Association.
The project team created several documents as part of this pilot project’s planning process:
Three hospital systems and two pediatric hospitals participated in the project, for a total of 22 hospitals. The hospitals included 17 general hospitals and five pediatric hospitals. The two children’s hospitals are also teaching institutions. Half of the 22 hospitals have 200 or more beds. Table 5 provides a description of the hospitals including the type of the hospital and the number of beds.
We were privileged that the people we worked with in the participating hospitals appreciated the value of this project, were interested in the results of the analysis, and were already involved in measuring their own quality measures. Moreover, learning the LOINC mapping process experience was of great interest to the hospitals.
Initially, we offered to share the datasets we created with the participating hospitals in addition to the analysis of the data for all of the hospitals combined, and a hospital-specific analysis for each hospital. After 3M HIS joined the team, the 3M research team director offered to provide the hospitals with individual analyses and the result per hospital and/or hospital system. Moreover, the 3M HIS Medical Director, Dr. Norbert Goldfield, proposed conducting consultations with the hospitals to interpret the results from the data analysis and offer any other explanations needed. Also, throughout the project we shared the monthly progress reports with the hospitals and we sent them a draft of this final report for their feedback and comments.
Table 5: Description of Participating Hospitals
|Hospitals||Type of Hospital||Number of Beds||Admissions in Project Period|
|Broward General Medical Center||Medical Center||716||21,896|
|Coral Springs Medical Center||Medical Center||200||9,876|
|Imperial Point Medical Center||Medical Center||204||5,318|
|North Broward Medical Center||Medical Center||409||10,120|
|Chris Evert Children's Hospital||Pediatric Medical Center||141|
|BayCare Health System|
|Mease Countryside Hospital||Community||300||12,929|
|Mease Dunedin Hospital||Community||143||4,793|
|Morton Plant Hospital||Community||687||23,662|
|Morton Plant North Bay Hospital||Community||122||4,838|
|St. Anthony's Hospital||Community||365||8,158|
|St. Joseph's Hospital||Community||527|
|St. Joseph's Children's Hospital||Children’s||164||37,213|
|St. Joseph's Women's Hospital||Women’s||192|
|South Florida Baptist Hospital||Community||147||4,524|
|Memorial Healthcare System|
|Memorial Hospital Miramar||Community||100||8,142|
|Memorial Hospital Pembroke||Community||301||5,185|
|Memorial Hospital West||Community||236||20,405|
|Memorial Regional Hospital||Community||690|
|Memorial Regional Hospital South||Community||100||28,401|
|Joe DiMaggio Children’s Hospital (http://www.jdch.com/)||Community||100||28,401|
|Miami Children's Hospital||Children’s||268||12,060|
|All Children’s Hospital||Children’s||216||5,947|
The project managers maintained ongoing communication via emails, conference calls and face-to-face meetings throughout the duration of this pilot project. All hospital teams indicated that in general these communication processes were efficient and useful. In particular, the calls that included all of the hospitals were very useful to them. During these calls, hospital staff were able to compare issues that had come up during the LOINC mapping and trade techniques for overcoming problems. To provide more effective communication, one hospital recommended having a more structured conference call format; another hospital suggested that scheduling face-to-face meetings with the hospital teams, the Agency and 3M to address the LOINC mapping processes would have been beneficial.
Adjusting the project timeline was a continuous task. Although delays were anticipated, the project team encountered many unforeseen setbacks. Beginning with budget approval, then waiting for the contract approval with 3M HIS, the project ran into delays in having all participating hospitals working on the project, to completing the LOINC mapping process and to the final analysis of the data.
When the project started the plan was to use HL7 for the transmitting the datasets to the Agency. Hospitals typically use HL7 to send documents, so it was assumed that this method would be the best for submitting their data. However, in discussing this with the CIOs of several hospital systems, they recommended that it would be better to send the data using Excel or as text files rather than HL7. They noted that the dataset would be a retrospective file containing thousands of laboratory records and that coding the data into HL7 would consume more resources than just sending the entire file. So we reached an agreement that the hospitals would send their data in Tab Separated Value text format using a secure File Transfer Protocol (FTP) site set up by the Agency.
The original proposal had planned to recruit 15 hospitals for the project, and the budget for the project was predicated on 3M TCS having to do LOINC mapping of 15 different clinical lab extracts. Instead, since the participating hospitals consisted of three hospital systems and two hospitals; 3M TCS had to conduct LOINC mapping and standardization for five clinical lab extracts.
In its implementation plan the project team proposed contracting with an academic researcher to conduct an independent analysis of the laboratory data joined to the administrative data. The One of the participating hospitals did not agree on sharing the data with any subcontractor other than the one stated in the original contract, 3M HIS. Also, the FSU College of Medicine declined working with the project because of the lack of time required to complete the analysis. Therefore, the project team cancelled that task.
Project implementation began once the AHCA project team had completed its face-to-face meetings with the hospital project staff, had distributed all of the background materials and the 3M Terminology Consulting Services consultant had held the initial introductory webinar on LOINC mapping. The AHCA project team distributed a survey after the LOINC mapping was completed to determine the resource requirements for standardizing the lab data. Results reported below come from that survey.
Most hospitals utilized their IT team for extracting the data and for using the Agency’s secure FTP site for uploading the data. The number of hours each participating hospital’s personnel spent on this pilot project varied from 33 hours to 132 hours. In general most of the time spent was by the IT or systems analyst team members as shown in Table 6. (For simplicity, the term hospital refers both to one hospital and to a hospital system.). These numbers are based on a LOINC mapping survey of the hospitals following successful submission of data.
Table 6: Personnel Involved in this project: Title, Tasks, and Number of Hours Spent
|Hospitals||Personnel Title||Task performed||Number of Hours|
|Miami Children's||VP of Information Technology||Project Manager||30|
|VP of Medical Affairs||Executive Sponsor||30|
|I/T Sr. Systems Analyst||Program download||40|
|Broward Health||Consulting systems analyst||Procedure mapping; create the data catalog, and data extraction||21|
|Administrative Support||Attended Conference calls and meetings||12|
|BayCare Health System||Manager LIS||Sample Data extract and LOINC mapping, point person for questions from other teams||20|
|CCL team||Modified and ran scripts to extract data and create the data catalog||16|
|Database||Security and FTP||5|
|Security team||Opened ports for FTP||1|
|Cerner Corporate Support||Helped with some database issues||3|
|Memorial Healthcare||Manager, IT Clinical Systems||Data extract||100|
|All Children's Hospital||Manager, Revenue Cycle Applications.||FTP files||2|
|Lab System support analyst||Data extraction||10|
|Outcomes Research Manager CV||Project Coordination||120|
The time required to complete the LOINC mapping varied across hospitals. BayCare Health System initially had its programming and operations teams working with the systems analyst but when they found out that this was essentially a one-time data submission, only a single programming resource was required. Additionally, BayCare Health System partnered with Broward Health, which had already written a LOINC mapping script to standardize the lab data, because both hospitals used the same Cerner Millennium Lab Information System (LIS). Baycare Health System was able to employ the mapping script for its LIS code values, thus decreasing the data translation time. The total number of hours required by each hospital to map and translate lab values into the LOINC dataset is shown in Figure 4.
Figure 4: Time Spent on Mapping Lab Values to LOINC per Hospital
Miami Children’s Hospital – 132 hours
All Children’s Hospital – 100 hours
Memorial Healthcare – 100 hours
BayCare Health System – 45 hours
Broward Health – 33 hours
The Agency’s project team developed an evaluation survey and sent it to the participating hospitals to gather feedback related on their experience with the project, the resources required for LOINC mapping and other information about the project (see Appendix 12). The survey consisted of 20 questions that addressed the hospital description, resources needed, data compilation, LOINC mapping, data transmission, communication tools, barriers encountered and their resolutions, and the lessons learned.
The following sections include the compilation of the hospitals’ responses and feedback. They are displayed in the same format of the evaluation survey. Each question is followed by the answers provided by the participating hospitals.
The 3M Terminology Consulting Services consultant worked closely with each of the hospitals to develop coding dictionaries of the laboratory tests and to map the test results to LOINC (see Appendix 13). For more LOINC information, a detailed user’s guide, and future updates are available at www.loinc.org. The vocabulary standard is used internationally to specify laboratory results and clinical observations in a standard format. The use of LOINC allows the integration of laboratory data from different Laboratory Information Systems into a single dataset. It is used in the healthcare industry by hospitals, laboratories, public health departments, integrated delivery networks, health plans and health information exchanges. According to the American Medical Informatics Association, there were 9,500 downloads of the LOINC standard from 86 different countries in 2008.
In order for 3M to provide the specific LOINC codes for this project, an orientation meeting was held between the 3M subject matter expert and the hospital teams to introduce the project and to explain the LOINC coding to them. This initial teleconference consisted of a PowerPoint presentation to introduce them to LOINC (see Appendix14). The six attributes encompassing a LOINC code are not recorded as such in a Laboratory Information System (LIS). Often, a translation needs to take place between information that is in the LIS, in order to obtain the appropriate LOINC code. An example of LOINC coding used in the initial training is shown in Figure 5.
Figure 5. The Anatomy of a LOINC Term
Anatomy of a LOINC term
Component: Property: Timing: Sample: Scale: Method
5193-8 – LOINC code; sequential number plus check digit
HEPATITIS B SURFACE AB: ACNC: PT: SER: QN: EIA
HEPATITIS B SURFACE AB is the component
ACNC is the property
PT is the timing
SER is the sample
QN is the scale
EIA is the method
3M Terminology Consulting Services asked for the following information on the approved lab tests for this project:
See Appendix 7 for an example extract along with a sample LOINC report. This was provided as part of the introduction for each of the sites, to envision their output for the LOINC mapping part of the project. More information related to LOINC is contained in Appendices 13, 17a, 17b, 19a, and 19b.
When the laboratory result nomenclature was compared among the participating hospitals, the need for LOINC standardization became clear. Each hospital used its own coding system to report the laboratory test, as is shown in Table 7.
Table 7. Comparison of Hospital Naming Conventions for Laboratory Tests
|Lab Test Name||All Children’s||Miami Children’s||BayCare||Broward Healthcare||Memorial Healthcare|
|Alkaline phosphatase||AP||Alkaline Phos||Alk Phos||55548696||ALKP|
|Blood/Lymph Culture-Positive||BCECMO||Blood Culture||C Blood||C BLD||CXBLD|
The hospitals each reported the process steps they used for the team to perform the LOINC mapping requirements of this project. These included developing a coding dictionary, submitting the data elements for mapping, then revising the LOINC mapping. The steps are presented in Table 8, from the Agency’s LOINC mapping survey of the hospitals and are their own descriptions of the LOINC Mapping process. Each hospital had to pull staff resources from other projects to complete the requirements of this pilot project, so time was tight and the descriptions are generally brief.
Each of the hospitals reported a different set of steps for the LOINC mapping, but each describes the translation process in the similar fashion. Broward Health had experience with LOINC prior to the project and offered the briefest description. BayCare Health System described the iterative process that their project team used with the 3M consultant. Yet both hospitals reported the least time to completion because they both had a Cerner Millennium LIS and both systems used the same LOINC mapping. Memorial followed a similar iterative process as it worked with the 3M consultant to map the laboratory data.
All Children’s Hospital followed a highly iterative pattern to map and translate their laboratory values to LOINC. They maintained the most interactive communications with the 3M LOINC consultant to clarify the requested procedures and to update the definitions. They also used more staff resources, as more people were brought into the project. Part of the time required by All Children’s Hospital process was taken up briefing new team members. These different descriptions point to the steep learning curve required to learn the LOINC mapping process and apply to laboratory test results. They demonstrate that the same LOINC mapping can be used by hospitals with similar Laboratory Information Systems.
Table 8. Steps in the LOINC Mapping Processes of Participating Hospitals
|Hospital||Steps in the LOINC Mapping Process|
|Broward Health||Map requested procedures to current reference data|
|BayCare Health System||Data looked up manually in system for requested tests
Spreadsheet of data completed
We added the LOINC codes that we could
Spreadsheet sent to Pam Banning for review
Received spreadsheet back from Pam with a few questions
Researched questions and responded
Received completed spreadsheet back from Pam
Supplied CCL team with list of LOINC codes to use
|Memorial Healthcare||Submission of data to 3m
Creation of a translation table
Crosswalk between data extract and translation table
|All Children's Hospital||Multiple teleconferences with AHCA and 3M Staff to coordinate project timelines, data extraction requirements, data parameters, and to resolve outstanding issues.
Multiple reviews and team meetings of AHCA and 3M project guidelines
From this relatively small sample of hospitals, two distinct approaches to LOINC mapping and translation stand out. One approach was based on existing knowledge of LOINC coding and on the capability to complete the LOINC mapping in-house without the help of the expert. The corollary use of expertise allowed LOINC mapping using the final mapping report of the expert hospital. The two hospitals in this group translated about 90% of the lab values into LOINC codes correctly and only 10% of the translations had to be by 3M’s consultant.
The second approach demonstrates the need for extensive training and communication as the hospitals learn the LOINC mapping process. In all cases, a strong LOINC mapping training component was essential. The hospitals that needed more training indicated that they could not have completed the LOINC mapping in-house on their own without the LOINC expert’s assistance. The LOINC training also worked in reverse, with one hospital updating three clinical procedures (blood culture, ionized calcium, and PO2) following 3M’s evaluation.
All participating hospitals indicated that they benefited from consultations with 3M LOINC mapping expert, who compiled the hospitals’ extract data and performed the actual mapping. They appreciated the explanations and clarifications of the LOINC coding and the meaning of the requested procedures. Mostly, they appreciated the professionalism displayed by 3M’s LOINC consultant, her flexibility, focus on the project completion and responsiveness in working with the hospital teams.
Most of the sites had not implemented LOINC in their systems prior to this project. One site had access to LOINC via a third party vendor hosting their physician’s office portal. There was an initial phase to map each site’s laboratory definitions to the LOINC vocabulary standard. The test catalog or compendium resides in the laboratory information system, without attachment to patient data.
Eight weeks were initially projected to complete the LOINC mapping. Three sites were actually mapped within three weeks. The other two sites had issues preventing them from submitting in same time period. They differed in workload from auditing one site’s LOINC mapping in two days to the last site requiring two months to submit the data, as shown in Table 9. The site taking the longest time had the greatest number of time constraints on providing data to map. Information filtered in from the site over the course of six weeks. Questions and confirmations were not answered; 3M eventually closed the work.
Table 9. Timeline of Project by Hospital
|Hospitals||Date Site Submitted||Date Initial Report||Date Completed|
|Memorial Healthcare||10/1/08 with follow-ups on 10/10/08 (troponin) and 10/14/08 (O2 Sat)||10/10/08||10/15/08|
|Broward Health||10/10/08||10/13/08 Except for O2 Sat||10/17/08|
|All Children’s Hospital||12/2/08 (Chemistry only)
12/23/08 (more labs)
1/14/09 (further labs)
|BayCare Health System||6/1/09 - Attempted their own mapping||6/2/09||6/3/09|
The 3M Terminology Consulting Services consultant submitted notes to the project team on a regular basis to provide updates on the LOINC mapping process. A summary of the notes is presented below, to indicate the technical nature of the LOINC mapping process and how each hospital had challenges unique to its laboratory information system.
All of the hospitals participated in the initial conference call with 3M and the Agency in which they were introduced to 3M’s team and they were provided with a list of the required data elements. From that date on, hospitals worked independently and at their own pace, from submitting their data catalog to 3M to uploading their data on the FTP site. Table 10 represents the hospitals’ description of the process steps performed.
Table 10. Steps Performed by Each Hospital in the Perform Data Requirements of the Project
|Hospital||Steps for Data Submission|
|Miami Children's||Identifying the data elements to be captured
Data Specifications were submitted for review
Conference Calls and follow-up e-mails to address any questions/ issues with Data Requirements
Specifications finalized and extracts were then created and submitted via FTP
|Broward Health||Map requested procedures and other data elements to clinical data repository|
|BayCare Health System||Obtained script from Broward Health
Completed sample data extract and LOINC mapping
Modified script with our systems code values
Added confirmed LOINC codes to the scripts
Scripts were run against database and data stored
Security team opened ports
Database team sent the data via FTP
|Memorial Healthcare||Linking of LOINC data and AHCA hosp data to existing system tables
Extract of patient data from SoftLab database
Extract of Result data from SoftLab database
Conversion to required format and export
Upload to FTP site
|All Children's Hospital||Defining data parameters
Development of Access queries
Importing of Access table into Excel
Transmission of file
The issues the hospitals encountered in complying with the data requests varied from none, to time constraints and to the impact of pulling the data while upgrading their LIS system. The following Table 11 contains the barriers that some hospitals indicated they faced and the ways they resolved them. Also this table contains the lessons learned and the suggestions based on overcoming barriers to data submission. Note that Miami Children’s Hospital replied "none" to all of the barriers.
Several of the problems encountered by the participating hospitals were due to time constraints. As mentioned previously, the project duration was two years, which would have allowed the hospitals enough time to complete the data extraction and LOINC mapping tasks at their own pace, without stressing their resources. But due to the budget and contracting hurdles, we held our first conference call with all participating hospitals and 3M on October 1, 2008, one year after the Agency was granted the award from AHRQ. Also, other administrative obstacles have contributed to more delays. Because of delays over the data sharing agreement, BayCare Health System did not start working on the projects until May 2009.
Table 11. Various Barriers Encountered by Hospitals and How they were Resolved
|Barriers||How was issue resolved?||Lessons learned|
|Broward Health||Technological||Date range requested covered a different system than one in current use||Look up historical data catalog||Prefer to use current lab system data|
|Other commitments||Concurrent system upgrade project and move of servers off site||Extended time taken to complete|
|BayCare Health System||Staff||Time, Every team is under time constraints right now||A couple of other projects were put on the back burner|
|Technological||1. Amount of data being pulled back in report put a significant increase on system resources 2. We had the scripts error out twice after running for 20 hours due to the amount of data being returned||Scripts were broken up into smaller time frames and the scripts were run during off hours when system resources aren’t as high.||Scripts can use some fine tuning to run more efficient|
|Other commitments||This occurred during our phase 2 scheduled build period of our EMR project so resources were extremely tight.||Resources were pulled from build to complete the report|
|Memorial Healthcare||Staff||Time availability, staffing shortage||Staff worked in off hours|
|Technological||Database structure on lab system||Multiple extracts with links was required|
|Other issues||Definitions of data fields were changed during the course of the project.||Additional programming time was required to accommodate the change in data|
|All Children's||Staff||Coordination of multiple staff members and departments. Project approval by multiple departments||Interdepartmental coordination and cross collaboration used to secure project approval||Coordinate early and often|
|Technological||Patient Data unavailable for year requested (2007) without significant increase in data extraction efforts||Patient data extraction for 2008 was approved by AHCA and 3M||Stay flexible in order to achieve your goals|
For this pilot project the hospitals uploaded their data as tab separated value files. In general, the hospital CIOs agreed at the beginning of the project that sending the data as a text file would be much easier than sending it using HL7, because the dataset represented a one-time data pull that was ill-suited to an HL7 transfer. Formatting the dataset for HL7 would have required considerable effort and resources. They were satisfied with the secure File Transfer Protocol (FTP) transfer format because they could send submit the data to the Agency in a straightforward manner that required a minimum of resources.
Figure 6. LOINC Mapping and Data Transmission Timeline
LOINC mapping completed:
All Children’s Hospital – November 2008
Memorial Healthcare System – November 2008
Broward Health System – November 2008
Miami Children’s Hospital – April 2009
BayCare Health System – May 2009
Lab and blood culture data transfer to AHCA’s FTP site:
All Children’s Hospital – December 2008
Memorial Healthcare System – February 2009
Broward Health System – March 2009
Miami Children’s Hospital – April 2009
BayCare Health System – May 2009
Admin & clinical data transfer to 3M HIS’s FTP site:
All Children’s Hospital – January 2009
Memorial Healthcare System – March 2009
Broward Health System – March 2009
Miami Children’s Hospital – May 2009
BayCare Health System – June 2009
All of the hospitals that agreed to participate in this project provided data, though not all were on target with the adjusted timeline. We received the data from all of the hospitals by June 15 2009, while it was originally anticipated that we would receive them in July of 2008.
There were issues surrounding the use of a secure FTP server both within the Agency and with the hospitals. The use of a secure FTP site for hospitals to submit data is routinely used by the Agency; however there were a number of issues with the Agency’s FTP site that could have been avoided with better communications:
For the participating hospitals, hospital firewalls and policies contributed to problems with the secure FTP site. On the one hand, hospital teams could not download the FTP software because of firewalls and hospital policies against loading unauthorized software on hospital computers. Hospital firewalls also prevented them from connecting to the Agency’s secure FTP server. These problems required assistance from the IT departments in the hospitals, and required IT staff to take care of uploading the data.
In order to test the degree to which clinical laboratory data can improve the accuracy of the risk adjustment methods for comparing hospital mortality rates, a risk adjustment method that uses only administrative data must be selected and then modified by adding clinical laboratory data. The performance of the risk adjustment method can then be assessed with and without the clinical laboratory data.
For the purposes of this project, the All Patient Refined Diagnosis Related Groups (APR DRGs) were selected as the risk adjustment method for the administrative data because of its widespread use and because it was developed by 3M HIS. APR DRGs are currently used by the Agency for Healthcare Research and Quality (AHRQ), the Agency for Healthcare Administration (AHCA), the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) and many other organizations as the risk adjustment method for reporting inpatient outcomes, including mortality. This project extended the use of risk adjustment through APR DRGs by adding clinical laboratory data to the risk adjusted dataset, and then comparing the risk adjusted datasets with and without laboratory data for their ability to predict inpatient mortality. The project involved five steps:
The following research summary provides a brief overview of the methods employed by 3M HIS, and then describes the results and outcomes of the statistical analysis.
Before the start of the pilot project, the Agency and 3M research teams used a review of the literature and the clinical expertise at 3M HIS to select a set of laboratory tests results that were:
The selected laboratory tests were identified according to Logical Observation Identifiers Names and Codes (LOINC) standards, which allowed them to be identified by standardized codes in electronic reports. The data elements contained in the clinical laboratory dataset included the LOINC codes, test result, units of measure, date and time of the specimen, type of test performed, and reference range of the test. Each record in the clinical laboratory dataset included the unique patient discharge identification number that was included in the administrative dataset in order to link a patient’s clinical laboratory data with the associated administrative discharge data. Each LOINC code was associated with one of the selected clinical laboratory data elements, and some of the laboratory tests were associated with multiple LOINC codes. Over 11.7 million clinical laboratory test records were contained in the clinical laboratory dataset.
Administrative Data Exclusions
After compiling the linked administrative and clinical laboratory data sets, the 3M project team applied seven additional criteria to the administrative dataset. Applying the patient level data quality screening criteria to the administrative dataset, 34,913 discharges were excluded from the administrative dataset. The majority of the discharges excluded from the administrative dataset based on the data quality screening criteria were due to a hospital having a low percentage of linked lab data for a three month quarter of data.
For this project, 3M applied five specific criteria for evaluating the quality of the present on admission coding. This POA screening criteria was developed using administrative data from California, and applied to the Florida administrative data to ensure POA coding accuracy. All of the hospitals passed the POA data quality screen criteria.
The final administrative analysis dataset contained 188,555 discharges from the project hospitals for discharges from April 2007 through December 2007.
Clinical Laboratory Data Exclusions
Over 11.7 million clinical laboratory data records were provided from hospitals participating in the study. Clinical laboratory data records that did not link to the 188,555 administrative discharge records in the analysis file were excluded. The remaining clinical laboratory data records were reviewed for data quality.
Each of the laboratory test records in the clinical laboratory dataset was standardized to a LOINC code using the mapping file developed by 3M HIS specific to the hospitals within each health system and to the children’s hospitals. Inconsistent laboratory test results were then identified and excluded. The frequency of the laboratory test result values was also examined and extreme or error test results for each of the specific clinical laboratory data element were identified and excluded.
After creating the linked administrative and clinical laboratory test data set, the next step was to create test result ranges for each of the laboratory tests that could be evaluated for their ability to improve the APR DRG prediction of mortality.
The 3M research team reviewed the distribution of test results for each individual LOINC code across hospitals and determined that the variation in both the reference (normal) ranges and the overall distribution of results was not significant. Therefore, the normal ranges did not require modification in order to be comparable across hospitals, and the actual numeric laboratory test result values were used directly in the analysis.
For each of the clinical laboratory data elements retained in the study, the 3M project team categorized the test results into clinically determined test result range categories, based on clinical judgment and literature review. They hypothesized that the test ranges that deviated most from normal would tend to correlate with higher mortality rates. The 3M team tested this hypothesis by examining the ability of test result ranges for each laboratory test to predict mortality when combined with APR DRGs.
The 3M team agreed with the overall philosophical approach of prior research that used laboratory values for improved risk of mortality prediction based on diagnoses/procedures present on admission; the challenge was in operationalizing this approach. There are several possible methods for selecting an admission laboratory value. Based on the information provided in the dataset, the 3M project team selected the first test result available for patient discharges with multiple test results for the same clinical laboratory data element to be included in the clinical laboratory data analysis file.
The next step was to determine which of the laboratory tests and their test result ranges added predictive value to the existing APR DRGs, and to incorporate them into the APR DRG logic. Risk adjusted models were created and analyzed using the following hospital administrative and clinical laboratory dataset models:
The 3M project team then examined the effect of individual laboratory tests and test result ranges within various patient groups, including individual APR DRGs, entire Major Diagnostic Categories (MDC), all surgical APR DRGs or all medical APR DRGs, or the entire patient population, in order to identify those laboratory tests associated with of higher risk of mortality. Indirect rate standardization was used to generate a set of reports that were used to evaluate the impact of clinical laboratory data on the four risk of morality subclasses. The clinical hypothesis tested was that for certain categories of patients the risk of mortality subclass could be increased based on the value of specific clinical laboratory results.
The literature which assesses the ability of various models to predict mortality relies on two basic statistics: reduction of variance (R2) and the area under the receiver operating characteristics (ROC) curve. In order to be consistent with this literature, the same two statistics were used for evaluating the ability of the APR DRG system to predict inpatient mortality with Florida data.
Case-level comparison of the baseline model A (using only administrative data) to model "B" (including the secondary diagnosis present on admission indicator) and model "C" (combining model B with laboratory test results) were performed using the c-statistic and R2. The c-statistic summarizes the ability of the Admission APR DRG and risk of mortality models to discriminate between patients who were discharged alive or dead. The R2 also summarizes the degree of error inherent in the Admission APR DRG and risk of the mortality models' ability to predict individual deaths.
The 3M research team next incorporated the results of the analysis into an APR DRG research prototype grouper. Each model was run against the Florida analysis dataset. Case level c-statistics and R2 were computed for each model separately. These reports and statistics were reviewed by the clinical panel to determine which clinical laboratory attributes should be recommended for incorporation into the APR DRG risk of mortality model. Once the individual clinical laboratory data element models for inclusion into the APR DRG model were identified, the APR DRG research prototype was developed to include all the additional recommended clinical laboratory modifications for a final evaluation of Model "C", and case level statistics were recomputed. See Appendix 21 for the final 3M HIS Analysis and Results Report.
The 3M HIS clinical panel reviewed the impact reports and determined potential modifications to the APR DRG risk of mortality subclass assignment algorithm. Based on a review of the mortality impact reports, the final clinical laboratory model ("Model C") included adjustments based on eleven clinical laboratory data elements. The adjustments to the risk of mortality assignment were specific to selected abnormal test result ranges and applied overall to all cases, or cases that belonged to specific clinical subgroups, including medical DRGs, surgical DRGs, or a specific MDC. The presence of a specified abnormal test result range category increased the risk of mortality level by one subclass to a specified maximum risk of mortality subclass.
Specifications for thirty-two adjustments to the risk of mortality subclass algorithm were defined. Overall, 18,057 (9.58%) patients were impacted by the addition of clinical laboratory data elements in the Admission APR DRG risk of mortality assignment. Blood urea nitrogen, Albumin and pCO2 made up the vast majority of changes to the Admission APR DRG risk of mortality assignment representing 8,657, 6,655, and 1,989 patients, respectively.
The c-statistic and R2 for mortality were computed based on the APR DRG and risk of mortality classification as defined by the three clinical models A, B and C, as described in the methods section. The removal of post-admission complications from the APR DRG and ROM assignment in clinical model "A" to clinical model "B" results in a percent decrease of 1.23% and 12.66% in the c-statistic and R2, respectively. The addition of the clinical laboratory data to the assignment of the Admission APR DRG and ROM subclass in model "C" relative to model "B" resulted in a percent increase of 0.574% and 4.53% in the c-statistic and R2 respectively.
For each of the thirty-two clinical laboratory adjustment to the risk of mortality subclass algorithm, the c-statistic and R2 were independently calculated. The percent change in c-statistic and R2 from the Admission APR DRG ROM clinical model ("Model B") were reviewed. Four clinical laboratory data element abnormal TRR category adjustment specifications had the largest impact on the overall increase in the results. pH < 7.1, Bicarbonate 10-15 and < 10, and Blood urea nitrogen had a percent increase in R2 of 4.41, 3.16, 2.86 and 1.07 respectively.
Because of the increasing importance and scrutiny of public reporting of inpatient outcomes and pay-for-performance initiatives, the risk adjustment method used in the comparison hospital outcome rates such as mortality must accurately describe a hospital’s case mix. Applications of risk adjusted mortality rates currently use the discharge APR DRG and risk of mortality subclass that includes all secondary diagnoses including those that develop during the hospital stay. However, the assessment of inpatient risk of mortality should ideally be based on a patient’s condition at the time of admission. The challenge is to give hospitals credit for diseases and conditions that represent a natural progression of the patient's underlying problem, but not to give credit for preventable complications.
In this study, which partially addresses this issue, the Admission APR DRG and risk of mortality subclass was computed using the present on admission indicator in order to remove any bias introduced by the inclusion of preventable complications in the risk assessment (partially in the sense that there may be some secondary diagnoses that occur after admission that should be included in the ROM assessment). While the statistical performance of the Admission APR DRG is lower than the Discharge APR DRG, the decrease in predictive power is relatively small and the APR DRG risk of mortality adjustment remained high even when the confounding effect of post admission complications was removed. In large measure this is due to the fact that the APR DRGs are a detailed clinical model and, for example, take into account the interaction between secondary diagnoses. The slight reduction in predictive power for the Admission APR DRG risk of mortality demonstrates that the models based on APR DRG risk of mortality derive their predictive power primarily from the diagnostic information present at admission and clinical stratification, and not from post admission complications. An important evaluation criteria for any risk of mortality system, is the extent to which the statistical performance of the system is dependent on the inclusion of post admission complications.
Since laboratory test results are not currently collected in administrative data, there will be considerable effort and cost associated with any mandate to report laboratory test results. To justify such costs the operational value of the laboratory test results must be demonstrated. This study demonstrated the value of selected laboratory results for enhancing the prediction of patient mortality. This preliminary study identified laboratory tests that are relevant for APR DRG Risk of Morality prediction and therefore should constitute the minimum scope of laboratory test results that are included in any mandated collection of selected laboratory test results.
In order to facilitate the collection of selected laboratory test results, this type of additional information could be collected in a manner more consistent with the existing ICD-9-CM diagnosis coding and reporting practices. A discrete set of "codes" could be defined for a select set of laboratory test results to provide a means for collecting additional patient characteristics in a way that does not require existing claims forms or claims processing systems to be modified.
The actual work on the first task involving the hospitals, such as data standardization and LOINC mapping, started a year after The Agency was granted the contract. This pilot project showed that after the recruitment process is completed, one year not sufficient to complete required tasks. The LOINC mapping of the selected clinical elements, the data extraction, the transfer of data, the merging of Administrative and clinical data were completed in nine months, but the data analysis required six months at a minimum. The list summarizes some of the lessons learned in the process of completing the various tasks of this pilot project.
The success of this project was due to working with the right people and maintaining ongoing communications with all members of the project. We had tremendous commitment from the participating hospitals and support from AHCA and 3M HIS. The hospitals working with us found the project interesting and useful for their future laboratory reporting. Once all the legal issues were ironed out for contacts and data exchange, and we received the go-ahead with the project, all of the teams came together to complete the project in a timely fashion. One hospital system indicated that having a Master Patient Index for all of its hospitals in a single lab system, centralized support from the lab system and open data base connectivity with the lab system database were the key for its successful participation.
In summary, a major determinant of the success of this pilot project came from the flexibility, collaboration, cooperation, dedication, perseverance, and coordination of all parties involved in the project. We hope the project develops into an on-going data feed to help further assess how clinical data can be used to improve the quality of healthcare for all Floridians.
|Internet Citation: Florida Final Report. Healthcare Cost and Utilization Project (HCUP). 07 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/datainnovations/clinicaldata/FL4AHRQProjectFinalReportMar2010_v2.jsp.|
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